OBJECTIVE: To identify patient- and practice-related factors associated with potential drug interactions. METHODS: A register analysis study in general practices in the county of Funen, Denmark. Prescription data were retrieved from a population-based prescription database (Odense University Pharmacoepidemiologic Database, OPED) covering prescriptions to all inhabitants in the county of Funen, Denmark. All individuals exposed to concurrent use of two or more drugs (polypharmacy) were identified. Combinations of drugs with potential interactions were registered and classified as major, moderate, or minor, depending on the severity of outcome and the quality of documentation. A two-level random coefficient logistic regression model was used to investigate factors related to potential drug interactions. RESULTS: One-third of the population was exposed to polypharmacy, and 6% were exposed to potential drug interactions during 1 year. Patient factors associated with increased risk of potential drug interactions were high age, a high number of concurrently used drugs, and a high number of prescribers. Practice factors associated with potential drug interactions were a high percentage of elderly patients and a low percentage of female patients listed. CONCLUSION: Prescription data may be useful in quality-improvement programmes to identify groups of patients and practices at increased risk of drug interactions.
OBJECTIVE: To identify patient- and practice-related factors associated with potential drug interactions. METHODS: A register analysis study in general practices in the county of Funen, Denmark. Prescription data were retrieved from a population-based prescription database (Odense University Pharmacoepidemiologic Database, OPED) covering prescriptions to all inhabitants in the county of Funen, Denmark. All individuals exposed to concurrent use of two or more drugs (polypharmacy) were identified. Combinations of drugs with potential interactions were registered and classified as major, moderate, or minor, depending on the severity of outcome and the quality of documentation. A two-level random coefficient logistic regression model was used to investigate factors related to potential drug interactions. RESULTS: One-third of the population was exposed to polypharmacy, and 6% were exposed to potential drug interactions during 1 year. Patient factors associated with increased risk of potential drug interactions were high age, a high number of concurrently used drugs, and a high number of prescribers. Practice factors associated with potential drug interactions were a high percentage of elderly patients and a low percentage of female patients listed. CONCLUSION: Prescription data may be useful in quality-improvement programmes to identify groups of patients and practices at increased risk of drug interactions.
Authors: A Freytag; R Quinzler; M Freitag; H Bickel; A Fuchs; H Hansen; S Hoefels; H-H König; K Mergenthal; S G Riedel-Heller; G Schön; S Weyerer; K Wegscheider; M Scherer; H van den Bussche; W E Haefeli; J Gensichen Journal: Schmerz Date: 2014-04 Impact factor: 1.107
Authors: Benjamin Lieske; Ingrid Becker; Ralf Joachim Schulz; Maria Cristina Polidori; Jan Kassubek; Gabriele Roehrig Journal: Z Gerontol Geriatr Date: 2015-11-23 Impact factor: 1.281
Authors: Olga Morales-Ríos; Luis Jasso-Gutiérrez; Alfonso Reyes-López; Juan Garduño-Espinosa; Onofre Muñoz-Hernández Journal: PLoS One Date: 2018-01-05 Impact factor: 3.240